Electrical power converters are used in a wide range of applications, including variable speed drives, grid-connected power converters, and DC-to-DC converters. There are several methods for controlling electrical converters. One such method is current control with reference tracking for switching power converters, in which a controller aims at regulating the converter currents along given current reference trajectories.
Efficiency is a key aspect in power electronics and electrical drive systems. To reduce losses, semiconductor switches are used and the power converter control variables may be discrete-valued. However, this may increase the complexity of these systems and may make the control of switching power converters inherently difficult. Evaluating the effect of the switching policies on variables of interest, such as currents and torque, may become highly non-trivial.
Known control methods for power converters include PI-controllers with pulse width modulation, hysteresis-based methods, and various sampled-data control algorithms in which the impact of the manipulated variable at the next sampling instant is examined. The latter approaches include, for example, deadbeat control and direct torque control.
Another strategy is Model Predictive Control (MPC), which has had a major impact on industrial process control and has also found its way into the control of switching power converters. MPC may be used in a variety of topologies and operating conditions, its flexibility stemming from the on-line optimization of a suitable cost function. Direct MPC (also called Finite-set MPC) methods may tackle the current control and modulation problem in one computational stage. During transients, MPC achieves a very high dynamic performance, similar to the one of dead-beat control. The transient performance of MPC may be by far superior to the one which can be achieved with optimized pulse patterns (OPPs), since traditionally, it has only been possible to use OPPs in a modulator driven by a very slow control loop.
Usually, an MPC formulation may give a better performance if longer prediction horizons are used. Unfortunately, direct MPC with long horizons for control of power converters may be computationally challenging, or even infeasible, since the number of possible switching sequences grows exponentially as the horizon length is increased. Thus, enumeration may be applicable only to MPC problems featuring a small number of switching sequences. Exhaustive enumeration may not be practical for problems with thousands of sequences, which may arise from direct MPC with prediction horizons of four or more. For example, for a prediction horizon of length five, assuming a three-level converter, the number of switching sequences may amount to 1.4*107.